Abstract
Information graphics (such as bar charts and line graphs) in popular media generally convey a message. This paper presents our approach to a significant problem in extending our message recognition system to line graphs — namely, the segmentation of the graph into a sequence of visually distinguishable trends. We use decision tree induction on attributes derived from statistical tests and features of the graphic. This work is part of a long-term project to summarize multimodal documents and to make them accessible to blind individuals.
This material is based upon work supported by the National Science Foundation under Grant No. IIS-0534948.
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Wu, P., Carberry, S., Chester, D., Elzer, S. (2008). Decision Tree Induction for Identifying Trends in Line Graphs. In: An, A., Matwin, S., Raś, Z.W., Ślęzak, D. (eds) Foundations of Intelligent Systems. ISMIS 2008. Lecture Notes in Computer Science(), vol 4994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68123-6_43
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DOI: https://doi.org/10.1007/978-3-540-68123-6_43
Publisher Name: Springer, Berlin, Heidelberg
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